Date of Award

8-2018

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer and Information Technology

Committee Chair

John A. Springer

Committee Member 1

William G. McCartney

Committee Member 2

Kevin C. Dittman

Abstract

The feld of data science is growing at break-neck speeds. More industries are looking towards data science professionals to fnd, compile, explore, and model data from numerous areas of interest. Currently, there are over 130,000 data science positions open. Professional training and institutions of higher education are unable to train enough individuals to place into these positions. Initial research in this space has started reviewing some of the data in this space, including determining the knowledge, skills, and abilities (KSAs) employers seek in data science candidates and what institutions are providing in the means of preparation in this space. With the general problem of too many data science positions and not enough professionals to fll them, this research intends to take a grounded theory approach to evaluating the data sources, looking for emergent theories that may provide additional understanding for how to solve that overarching problem.

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